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将二进制粒子群优化算法的惯性因子进行了动态化自适应改进,设计了区别于标准遗传操作的高频交叉算子和随机自回馈变异算子,基于此提出了一种新算法——混合粒子群智能遗传算法(PGA)应用与配网的重构。在新型编码方案下,PGA应用两个遗传算子使种群保持多样性,避免陷入局部最优,同时结合PSO的快速群体智能寻优指导染色体的进化方向,能够使种群信息共享的同时提高算法的收敛速度,算例结果验证了新算法的可行性。
The inertia factor of BPSO algorithm is improved dynamically and adaptively. The high frequency crossover operator and random self-feedback mutation operator which are different from the standard genetic operators are designed. Based on this, a new algorithm, hybrid particle Application of Swarm Intelligent Genetic Algorithm (PGA) and Distribution Network Reconstruction. Under the new encoding scheme, PGA uses two genetic operators to keep the diversity of the population and avoid falling into the local optimum, meanwhile, the rapid population intelligence optimization of PSO guides the evolution direction of the chromosome, which can make the population information share and improve the algorithm Convergence rate, the example results verify the feasibility of the new algorithm.